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Why does start_session() fail with a connection error?

Mellea’s default backend is Ollama. If Ollama is not running, any call that reaches the backend raises a connection error: 
ConnectionError: Could not connect to Ollama at http://localhost:11434
Start Ollama and try again: 
ollama serve
Verify the server is reachable before running your script: 
curl http://localhost:11434/api/version
If Ollama is running on a non-default host or port, pass the URL explicitly: 
from mellea.backends.ollama import OllamaModelBackend
from mellea import MelleaSession
from mellea.stdlib.context import SimpleContext

m = MelleaSession(
    backend=OllamaModelBackend(base_url="http://my-ollama-host:11434"),
    ctx=SimpleContext(),
)

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How do I use a model other than granite4:micro?

Pass the model_id parameter to start_session(): 
from mellea import start_session

with start_session(model_id="llama3.2:latest") as m:
    response = m.chat("What is 1+1?")
    print(response)
Pull the model with Ollama before using it: 
ollama pull llama3.2:latest
You can also pass a backend instance directly to MelleaSession for full control over backend options: 
from mellea import MelleaSession
from mellea.backends.ollama import OllamaModelBackend
from mellea.stdlib.context import SimpleContext

m = MelleaSession(
    backend=OllamaModelBackend("mistral:latest"),
    ctx=SimpleContext(),
)

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Can I use Mellea without Ollama?

Yes. Ollama is the default backend but not the only one. Mellea ships with backends for OpenAI-compatible APIs, HuggingFace local inference, IBM WatsonX, and LiteLLM (which itself proxies dozens of providers). Install the backend you need: 
pip install "mellea[litellm]"    # LiteLLM multi-provider
pip install "mellea[hf]"         # HuggingFace / local inference
pip install "mellea[watsonx]"    # IBM WatsonX
Then pass the backend to start_session() or MelleaSession: 
from mellea import start_session

# OpenAI
with start_session(backend_name="openai", model_id="gpt-4o") as m:
    print(m.chat("Hello!"))

# LiteLLM wrapping Anthropic
from mellea.backends.litellm import LiteLLMBackend
from mellea import MelleaSession
from mellea.stdlib.context import SimpleContext

m = MelleaSession(
    backend=LiteLLMBackend("anthropic/claude-3-5-sonnet-20241022"),
    ctx=SimpleContext(),
)
See Common Errors for help installing backend-specific dependencies.

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Why does my @generative function return the wrong type?

The @generative decorator uses the function’s docstring as the prompt. If the docstring is vague, the model may return output that cannot be parsed into the declared return type. Compare these two definitions: 
from mellea import generative

# Vague — the model may return extra explanation text
@generative
def extract_keywords(text: str) -> list[str]:
    """Extract keywords."""

# Specific — the model knows exactly what format is expected
@generative
def extract_keywords(text: str) -> list[str]:
    """Extract the five most important keywords from the text.
    Return only a Python list of strings with no extra commentary.
    Example output: ["machine learning", "neural networks", "training"]
    """
For stricter guarantees, add requirements: 
from mellea import generative, start_session
from mellea.stdlib.requirements import req

@generative
def classify(text: str) -> str:
    """Classify the sentiment of the text. Return only one word:
    positive, negative, or neutral."""

with start_session() as m:
    result = classify(
        m,
        text="This product is great!",
        requirements=[req("Must be one of: positive, negative, neutral")],
    )
If the function raises ComponentParseError, add an example to the docstring — the model needs a concrete illustration of the expected format.

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What is the difference between instruct() and @generative?

Both call the LLM, but they differ in when you write the prompt and how you pass variables. instruct() takes a prompt string with {{variable}} placeholders at call time. It is best for one-off instructions where the prompt text varies: 
from mellea import start_session

with start_session() as m:
    result = m.instruct(
        "Translate the following into {{language}}: {{text}}",
        user_variables={"language": "French", "text": "Hello, world!"},
    )
@generative defines the prompt once in the function’s docstring. It is best when you want a reusable, typed, unit-testable function: 
from mellea import generative, start_session

@generative
def translate(text: str, language: str) -> str:
    """Translate text into the specified language.
    Return only the translated text, with no explanation.
    """

with start_session() as m:
    result = translate(m, text="Hello, world!", language="French")
@generative functions also participate in Mellea’s lazy evaluation graph, which means you can feed a thunk from one generative call into another before either has been evaluated.

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Why do requirements keep failing?

When the model keeps retrying but the output looks correct, one of the following is usually the cause:
  • The requirement is too strict. A requirement like “Must be exactly 17 syllables” is difficult for a model to satisfy reliably. Relax the constraint or provide the model with more context.
  • The default budget is too low. instruct() defaults to loop_budget=2. Increase it: 
    from mellea import start_session
from mellea.stdlib.requirements import req
from mellea.stdlib.sampling import RejectionSamplingStrategy

with start_session() as m:
    result = m.instruct(
        "Write a haiku about autumn.",
        requirements=[req("Must be exactly 17 syllables")],
        strategy=RejectionSamplingStrategy(loop_budget=5),
    )
  • The validation function is wrong. If you are using a custom verifier, check it returns True for valid output. Use return_sampling_results=True to inspect each attempt: 
    result = m.instruct(
    "Write a haiku about autumn.",
    requirements=[req("Must be exactly 17 syllables")],
    return_sampling_results=True,
)
print(f"Success: {result.success}")
for attempt, (gen, vals) in enumerate(
    zip(result.sample_generations, result.sample_validations), 1
):
    print(f"Attempt {attempt}: {gen.value!r}")
    for requirement, validation in vals:
        print(f"  {requirement.description}: {validation._result}")

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How do I see what the model is actually receiving?

Use GenerateLog to capture the rendered prompt. Enable application tracing or backend tracing and check the response and gen_ai.usage.input_tokens attributes on the spans. For a quick local inspection without a trace backend, enable console tracing: 
export MELLEA_TRACE_BACKEND=true
export MELLEA_TRACE_CONSOLE=true
python your_script.py
Each backend span prints the operation name, model ID, and token counts. Alternatively, inspect the GenerateLog objects returned with sampling results: 
from mellea import start_session
from mellea.stdlib.requirements import req

with start_session() as m:
    result = m.instruct(
        "Summarise in one sentence: {{text}}",
        user_variables={"text": "Long article content here."},
        return_sampling_results=True,
    )
    for log in result.generate_logs:
        print(log.prompt)
        print(log.backend)
For the full telemetry setup, see OpenTelemetry Tracing.

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Does Mellea support async?

Yes. Every synchronous method has an async counterpart:
SyncAsync
m.chat()await m.achat()
m.instruct()await m.ainstruct()
m.act()await m.aact()
mfuncs.act()await mfuncs.aact()
@generative functions work in async context when you await them: 
import asyncio
from mellea import generative, start_session

@generative
async def summarise(text: str) -> str:
    """Summarise the text in one sentence."""

async def main() -> None:
    with start_session() as m:
        result = await summarise(m, text="Long article content here.")
        print(result)

asyncio.run(main())
Note: If you are inside a Jupyter notebook, the event loop is already running. Use await directly or install nest_asyncio to allow nested loops.

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How do I contribute?

Read the contributing guide first: 
cat docs/docs/guide/CONTRIBUTING.md
The short version:
  1. Fork the repository and clone it.
  2. Install dependencies: uv sync --all-extras --all-groups
  3. Install pre-commit hooks: pre-commit install
  4. Create a branch: git checkout -b feat/your-feature
  5. Run tests: uv run pytest -m "not qualitative"
  6. Open a pull request.
All commits use Angular format (feat:, fix:, docs:, refactor:). Pre-commit runs ruff, mypy, and codespell automatically.

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Where can I get help?

  • GitHub Issues: Report bugs and request features at the project’s GitHub Issues page.
  • GitHub Discussions: Ask questions and share ideas in the Discussions tab.
  • Examples: The docs/examples/ directory contains runnable examples covering every major feature.
  • Common Errors: See Common Errors for a reference table of known error messages and fixes.

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See also

  • Common Errors — a reference table of error messages, diagnostic steps, and fixes.
  • Quick Start — install Mellea and run your first generative function.